Addressing Gaps in Small-Scale Fisheries: A Low-Cost Tracking System

none5During the last decade vessel-position-recording devices, such as the Vessel Monitoring System and the Automatic Identification System, have increasingly given accurate spatial and quantitative information of industrial fisheries. On the other hand, small-scale fisheries (vessels below 12 m) remain untracked and largely unregulated even though they play an important socio-economic and cultural role in European waters and coastal communities and account for most of the total EU fishing fleet. The typically low-technological capacity of these small-scale fishing boats—for which space and power onboard are often limited—as well their reduced operative range encourage the development of efficient, low-cost, and low-burden tracking solutions. In this context, we designed a cost-effective and scalable prototypic architecture to gather and process positional data from small-scale vessels, making use of a LoRaWAN/cellular network. Data collected by our first installation are presented, as well as its preliminary processing. The emergence of a such low-cost and open-source technology coupled to artificial intelligence could open new opportunities for equipping small-scale vessels, collecting their trajectory data, and estimating their fishing effort (information which has historically not been present). It enables a new monitoring strategy that could effectively include small-scale fleets and support the design of new policies oriented to inform coastal resource and fisheries management.openAnna Nora Tassetti, Alessandro Galdelli, Jacopo Pulcinella, Adriano Mancini, Luca BologniniNora Tassetti, Anna; Galdelli, Alessandro; Pulcinella, Jacopo; Mancini, Adriano; Bolognini, Luc

Using AIS to Attempt a Quantitative Evaluation of Unobserved Trawling Activity in the Mediterranean Sea

In the past decades, the Automatic Identification System (AIS) has been employed in numerous research fields as a valuable tool for, among other things, Maritime Domain Awareness and Maritime Spatial Planning. In contrast, its use in fisheries management is hampered by coverage and transmission gaps. Transmission gaps may be due to technical limitations (e.g., weak signal or interference with other signals) or to deliberate switching off of the system, to conceal fishing activities. In either case such gaps may result in underestimating fishing effort and pressure. This study was undertaken to map and analyze bottom trawler transmission gaps in terms of duration and distance from the harbor with a view to quantifying unobserved fishing and its effects on overall trawling pressure. Here we present the first map of bottom trawler AIS transmission gaps in the Mediterranean Sea and a revised estimate of fishing effort if some gaps are considered as actual fishing

A Novel Remote Visual Inspection System for Bridge Predictive Maintenance

Predictive maintenance on infrastructures is currently a hot topic. Its importance is proportional to the damages resulting from the collapse of the infrastructure. Bridges, dams and tunnels are placed on top on the scale of severity of potential damages due to the fact that they can cause loss of lives. Traditional inspection methods are not objective, tied to the inspector’s experience and require human presence on site. To overpass the limits of the current technologies and methods, the authors of this paper developed a unique new concept: a remote visual inspection system to perform predictive maintenance on infrastructures such as bridges. This is based on the fusion between advanced robotic technologies and the Automated Visual Inspection that guarantees objective results, high-level of safety and low processing time of the results

Benchmarking of Dual-Step Neural Networks for Detection of Dangerous Weapons on Edge Devices

Nowadays, criminal activities involving hand-held weapons are widespread throughout the world and pose a significant problem for the community. The development of Video Surveillance Systems (VSV) and Artificial Intelligence (AI) approaches have made it possible to implement automatic systems for detecting dangerous weapons even in crowded environments. However, the detection of hand-held weapons - usually very small in size with respect to the Field of View (FoV) of the camera - is still an open challenge. The use of complex hardware systems and deep learning (DL) architectures have mitigated this problem and achieved excellent results, but involve high costs and high performance that hinder the deployment of such systems. In this contest, we present a comprehensive performance comparison in terms of inference time and detection accuracy of two low-cost edge devices: Google Coral Dev board and NVIDIA Jetson Nano. We deployed and run on both boards a dual-step DL framework for hand-held weapons detection exploiting half-precision floating-point (FP16) quantization on Jetson Nano and 8-bit signed integer (INT8) quantization on Coral Dev. Our results show that both in terms of PASCAL VOC mean Average Precision (mAP) and Frames per Second (FPS), the framework running on Jetson Nano (mAP = 99.6, FPS = 4.5, 2.5, 1.7, 1.4 from 1 to 4 people in the camera FoV, respectively) slightly outperform the Coral's one (mAP = 98.8 and FPS = 2.9. 1.5, 1.1, 0.9 from 1 to 4 people in the camera FoV, respectively). The Coral Dev obtained the highest inference speed (FPS = 36.5) overcoming the Jetson Nano (FPS=23.8) only when running the dual-step framework with no people in the camera FoV. In conclusion, the benchmark on the two edge devices points out that both allow to run the framework with satisfactory results, pushing towards the diffusion of such on-the-edge systems in a real-world scenario

Retail Robot Navigation: A Shopper Behavior-Centric Approach to Path Planning

In the ever-evolving landscape of retail, understanding shopper behavior is pivotal for optimizing sales and effectively managing product availability and placement. This study explores the integration of autonomous mobile robots into the shelf inspection process, leveraging advancements in automation, information, and robotics technology. Performing mapping tasks, these robots incorporate insights into customer behavior by exploiting various sources of behavioral data, including trajectories and product interactions. Motivated by the complex and dynamic nature of modern stores, our research seeks to bridge the gap in retail inventory management. Our unique contribution lies in the development of a novel path planning method for robots, specifically tailored for an automated inventory management system. By focusing on customer trajectories and product interactions, we aim to enhance the arrangement and positioning of products within retail spaces. Our research is motivated by the need to address the challenges faced by retailers in optimizing store layouts and product placements. The proposed strategy utilizes a heatmap and a vision-based system to analyze spatial and temporal patterns of shopper behavior. This information is then employed to optimize robot navigation in both highly and less-visited areas. Trajectories and product interactions data from real store installations were utilized in simulation, providing valuable insights into optimal planning for mobile robots to visit Points of Interest (PoI). The active shopping cart tracking system generated heatmaps, while a vision-based system collected shopper-products interactions data. Subsequently, our approach was deployed on a real retail robot for inventory management, and the path planning source code was released. Our findings demonstrate that the path planned by our approach not only avoids collisions with static store sections but also optimizes paths in areas with significant customer-shelf activity

A Synergic Integration of AIS Data and SAR Imagery to Monitor Fisheries and Detect Suspicious Activities

Maritime traffic and fishing activities have accelerated considerably over the last decade, with a consequent impact on the environment and marine resources. Meanwhile, a growing number of ship-reporting technologies and remote-sensing systems are generating an overwhelming amount of spatio-temporal and geographically distributed data related to large-scale vessels and their movements. Individual technologies have distinct limitations but, when combined, can provide a better view of what is happening at sea, lead to effectively monitor fishing activities, and help tackle the investigations of suspicious behaviors in close proximity of managed areas. The paper integrates non-cooperative Synthetic Aperture Radar (SAR) Sentinel-1 images and cooperative Automatic Identification System (AIS) data, by proposing two types of associations: (i) point-to-point and (ii) point-to-line. They allow the fusion of ship positions and highlight “suspicious” AIS data gaps in close proximity of managed areas that can be further investigated only once the vessel—and the gear it adopts—is known. This is addressed by a machine-learning approach based on the Fast Fourier Transform that classifies single sea trips. The approach is tested on a case study in the central Adriatic Sea, automatically reporting AIS-SAR associations and seeking ships that are not broadcasting their positions (intentionally or not). Results allow the discrimination of collaborative and non-collaborative ships, playing a key role in detecting potential suspect behaviors especially in close proximity of managed areas

Towards Groundwater-Level Prediction Using Prophet Forecasting Method by Exploiting a High-Resolution Hydrogeological Monitoring System

Forecasting of water availability has become of increasing interest in recent decades, especially due to growing human pressure and climate change, affecting groundwater resources towards a perceivable depletion. Numerous research papers developed at various spatial scales successfully investigated daily or seasonal groundwater level prediction starting from measured meteorological data (i.e., precipitation and temperature) and observed groundwater levels, by exploiting data-driven approaches. Barely a few research combine the meteorological variables and groundwater level data with unsaturated zone monitored variables (i.e., soil water content, soil temperature, and bulk electric conductivity), and—in most of these—the vadose zone is monitored only at a single depth. Our approach exploits a high spatial-temporal resolution hydrogeological monitoring system developed in the Conero Mt. Regional Park (central Italy) to predict groundwater level trends of a shallow aquifer exploited for drinking purposes. The field equipment consists of a thermo-pluviometric station, three volumetric water content, electric conductivity, and soil temperature probes in the vadose zone at 0.6 m, 0.9 m, and 1.7 m, respectively, and a piezometer instrumented with a permanent water-level probe. The monitored period started in January 2022, and the variables were recorded every fifteen minutes for more than one hydrologic year, except the groundwater level which was recorded on a daily scale. The developed model consists of three “virtual boxes” (i.e., atmosphere, unsaturated zone, and saturated zone) for which the hydrological variables characterizing each box were integrated into a time series forecasting model based on Prophet developed in the Python environment. Each measured parameter was tested for its influence on groundwater level prediction. The model was fine-tuned to an acceptable prediction (roughly 20% ahead of the monitored period). The quantitative analysis reveals that optimal results are achieved by expoiting the hydrological variables collected in the vadose zone at a depth of 1.7 m below ground level, with a Mean Absolute Error (MAE) of 0.189, a Mean Absolute Percentage Error (MAPE) of 0.062, a Root Mean Square Error (RMSE) of 0.244, and a Correlation coefficient of 0.923. This study stresses the importance of calibrating groundwater level prediction methods by exploring the hydrologic variables of the vadose zone in conjunction with those of the saturated zone and meteorological data, thus emphasizing the role of hydrologic time series forecasting as a challenging but vital aspect of optimizing groundwater management

Integrating AIS and SAR to monitor fisheries: a pilot study in the Adriatic Sea

The synergic utilization of data from different sources, either ground-based or spaceborne, can lead to effectively monitor fishing activities in close proximity of managed areas, and help tackle the problem of global overfishing. To this end, the integration of spaceborne Synthetic Aperture Radar (SAR) data and cooperative Automatic Identification System (AIS) information has the appealing potential to provide a better picture of what is happening at sea by detecting vessels that are not reporting their positioning data (intentionally or not) and, on the other side, by validating ships detected in satellite imagery. In this context, this paper deals with the investigation of "suspicious" AIS data gap and the integration of SAR-based ship detection by a point-to-point and a point-to-line types of association. Time-filtered and classified AIS transmissions (according to the gear in use) are used to predict SAR positions, with the next step being to search/match corresponding SAR-based targets. A case study is analyzed, in which the method is tested in proximity of managed areas characterized by significant AIS blackouts, using occasional Sentinel-1 images of the central Adriatic Sea and AIS dat